A gradient norm from a post-hoc self-supervised trajectory forecasting decoder detects distribution shifts in prediction models, with reported improvements on Shifts and Argoverse datasets.
Learning representations by back-propagating er- rors.nature, 323(6088):533–536, 1986
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Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction
A gradient norm from a post-hoc self-supervised trajectory forecasting decoder detects distribution shifts in prediction models, with reported improvements on Shifts and Argoverse datasets.